Show the code
#|
vision_count <- openalexR::oa_fetch(
title_and_abstract.search = compact(vision_st),
count_only = TRUE,
verbose = TRUE
)[, "count"]Data Management Report
A short description what this is about. This is not a tracditional abstract, but rather something else …
IPBES_TCA_Ch2_technology
%The BuidNo is automatically increased by one each time the report is rendered. It is used to indicate different renderings when the version stays the same%.
All searches are done on all works in OpenAlex. The search in the TCA Corpus is not possibly at the moment, but we are working on it.
The search terms are based on the shared google doc. They are cleaned up for the usage in OpenAlex.
The search terms is vision
#|
vision_count <- openalexR::oa_fetch(
title_and_abstract.search = compact(vision_st),
count_only = TRUE,
verbose = TRUE
)[, "count"]The search terms is technology
#|
technology_count <- openalexR::oa_fetch(
title_and_abstract.search = compact(technology_st),
count_only = TRUE,
verbose = TRUE
)[, "count"]The search term is vision AND technology
#|
vision_technology_count <-
openalexR::oa_fetch(
title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
count_only = TRUE,
verbose = TRUE
)[, "count"]#|
vision_technology_subfields <- openalexR::oa_query(
title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
group_by = "primary_topic.subfield.id",
verbose = TRUE
) |>
openalexR::oa_request() |>
dplyr::bind_rows() |>
dplyr::arrange(key)## clean up missing or wrong vision_technology_subfields$key_display_name
need_cleaning <- is.na(vision_technology_subfields$key_display_name) |
!is.na(as.numeric(vision_technology_subfields$key_display_name))Warning: NAs introduced by coercion
fine <- !need_cleaning
vision_technology_subfields <- vision_technology_subfields |>
dplyr::filter(fine) |>
dplyr::select(key, key_display_name) |>
dplyr::distinct() |>
merge(y = vision_technology_subfields[need_cleaning, -2], by = "key") |>
dplyr::bind_rows(vision_technology_subfields[fine, ]) |>
dplyr::group_by(key, key_display_name) |>
dplyr::summarize(count = sum(count))The subfields are based on the main topic assigned to each work. There are other topics also assigned, but this one has been identified as the main topic by an algorythm. count is the number of works in the vision AND technology corpus which have been assigned to the subfield.
Please take a look at these subfields of the topics to identify the ones to be filtered out.
The easies would be to download the Excel file through the button and to mark the subfields to be filtered out.
IPBES.R::table_dt(vision_technology_subfields, fixedColumns = NULL, fn = "Vision Technology Subfields")@report{krug,
author = {Krug, Rainer M.},
title = {Report {Assessment} {Ch2} {Technology} {Visions}},
doi = {XXXXXX},
langid = {en},
abstract = {A short description what this is about. This is not a
tracditional abstract, but rather something else ...}
}